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NASA NTRS1980

Open-loop nanosecond-synchronization for wideband satellite communications

A synchronization technique for use with an onboard processing satellite communication system is discussed. The satellite oscillator is used both as the system time reference and as the frequency source for all downlink carriers and data clocks. Downlink timing is established at each system earth terminal through a combination of carrier and data-clock tracking and a downlink timing epoch signal consisting of one bit per TDMA data burst. Uplink timing is established by an open-loop range prediction process using precision ephemerides calculated and distributed by the central control station. Overall timing accuracy of the uplink signal at the satellite receiver of + or - 7 nanoseconds permits unambiguous identification of each data bit position in a 128 Mbps TDMA burst. This is accomplished by means of simple, inexpensive terminal hardware using available crystal oscillators for time/frequency references and digital synthesis techniques that may be implemented in digital LSI chips.

Holmes, W. M., Jr.↗

NASA NTRS1984

LAMPS software

The dynamic prediction model along with its macro-processor capability and data flow system from the Drexel Limited-Area and Mesoscale Prediction System (LAMPS) were converted and recorded for the Perkin-Elmer 3220. The previous version of this model was written for Control Data Corporation 7600 and CRAY-1a computer environment which existed until recently at the National Center for Atmospheric Research. The purpose of this conversion was to prepare LAMPS for porting to computer environments other than that encountered at NCAR. The emphasis was shifted from programming tasks to model simulation and evaluation tests.

Perkey, D. J.↗

NASA NTRSDate not supplied

Experimental Testing of Advanced Generalized Predictive Control for Stability Augmentation and Vibration Reduction of Tiltrotor Aircraft

Generalized Predictive Control (GPC) is an advanced form of an adaptive control algorithm that uses experimentally acquired data to determine the input-output relationship of complex systems through a process called system identification (system ID). GPC has historically been applied to wind tunnel tests of dynamically-scaled tiltrotor aircraft for stability augmentation and vibration reduction since the complex nature of these dynamic systems does not lend itself well to traditional control theory. Advanced GPC (AGPC) improves upon traditional GPC by enabling self-adaptation as conditions change from those used to acquire the system ID and controller performance would normally erode. The present research expands upon previous analytical development and demonstration of AGPC with experimental demonstration. To support AGPC, this present work also identifies and describes figures of merit that define a good working controller and quantifies the uniqueness of the control inputs and quality of the system ID parameters. The present research demonstrates that AGPC consistently performs better than traditional GPC and can successfully adapt to changing conditions.

Active Controls↗

NASA NTRS1998

Correlation of Predicted and Observed Optical Properties of Multilayer Thermal Control Coatings

Thermal control coatings on spacecraft will be increasingly important, as spacecraft grow smaller and more compact. New thermal control coatings will be needed to meet the demanding requirements of next generation spacecraft. Computer programs are now available to design optical coatings and one such program was used to design several thermal control coatings consisting of alternating layers of WO3 and SiO2. The coatings were subsequently manufactured with electron beam evaporation and characterized with both optical and thermal techniques. Optical data were collected in both the visible region of the spectrum and the infrared. Predictions of solar absorptance and infrared emittance were successfully correlated to the observed thermal control properties. Functional performance of the coatings was verified in a bench top thermal vacuum chamber.

Jaworske, Donald A.↗

NASA NTRS2019

Circulating miRNA Signature Predicts Health Risks Associated with Radiation and Microgravity

The many known health risks currently associated with space travel include increased risk of cardiovascular disease, cancer, central nervous system related diseases, muscle degeneration, and changes with host-gut microbiome interactions that can have profound impact with these and other health risks. The majority of the risk from space travel stem of the two components of the space environment which are microgravity and radiation. From our earlier work (Beheshti et al, PLOS One, 2018), we predicted that there is a systemic component of the host that causes general increased health risks due to spaceflight driven by a circulating microRNA (miRNA) signature consisting of 13 miRNAs that directly regulates both p53 and TGF1. MiRNAs are small non-coding RNA molecules with a negative and post-transcriptional regulation on gene expression) are increasingly recognized as major systemic regulators of responses to stressors, including microgravity, oxidative stress, and DNA damage. In addition, due to the size and stability of miRNAs, it is known that miRNAs can circulate throughout the body and have been found in the majority of the bodily fluids including blood, urine, saliva, and tears. Here, we start to dissect the actual impact of this miRNA signature on both the radiation and microgravity components and prove that this miRNA signature actually exists in the circulation of a host. To achieve this, we obtained multiple tissues including, serum, liver, and spleen and utilizing droplet digital PCR (ddPCR), we start to show how this circulating miRNA signature impacts which component of the spaceflight. The tissue was obtained from experiments performed on C57BL/6 male mice (N=10 for each condition) that were hindlimb unloaded (HU) to simulated microgravity, irradiated with 2Gy gamma (IR), HU plus IR, and control mice under normal conditions. It was shown that these miRNAs were present in the serum as predicted by the in silico prediction from our earlier predictions. The HU vs Controls show significant increases of the predicted miRNAs in the serum for more than half of the miRNA signature, with remaining miRNAs increasing comparing to the controls close to statistical significance. IR vs control mice showed increases for the miRNAs, but not has pronounced as the HU conditions. Finally, the combination of the HU+IR vs controls showed increases for the majority of the miRNA signature. The data indicates that the miRNA signature originally predicted through in silico methods is mainly associated with the microgravity component and is circulating throughout the host resulting in a systemic impact of the miRNAs on the host. These miRNAs are shown in the literature to potentially increase health risks associated with several diseases. In addition, we have begun testing the potential of utilizing antagonists to this miRNA signature to act as a potential countermeasure to mitigate radiation impact on the organism. This work demonstrates for the first time the potential of a minimally invasive novel biomarker and countermeasure that can be used to mitigate both radiation and microgravity effects.

Beheshti, Afshin↗

NASA NTRSDate not supplied

Experimental Testing of Advanced Generalized Predictive Control for Stability Augmentation and Vibration Reduction of Tiltrotor Aircraft

Active Controls↗

NASA NTRS1991

The Langley Research Center CSI phase-0 evolutionary model testbed-design and experimental results

A testbed for the development of Controls Structures Interaction (CSI) technology is described. The design philosophy, capabilities, and early experimental results are presented to introduce some of the ongoing CSI research at NASA-Langley. The testbed, referred to as the Phase 0 version of the CSI Evolutionary model (CEM), is the first stage of model complexity designed to show the benefits of CSI technology and to identify weaknesses in current capabilities. Early closed loop test results have shown non-model based controllers can provide an order of magnitude increase in damping in the first few flexible vibration modes. Model based controllers for higher performance will need to be robust to model uncertainty as verified by System ID tests. Data are presented that show finite element model predictions of frequency differ from those obtained from tests. Plans are also presented for evolution of the CEM to study integrated controller and structure design as well as multiple payload dynamics.

Belvin, W. K.↗

NASA NTRS1994

Temperature prediction of space flight experiments by computer thermal analysis

Life sciences experiments are especially sensitive to temperature. A small temperature difference between otherwise identical samples can cause various differences in biological reaction rates. Knowledge of experimental temperatures and temperature histories help to distinguish the effects of microgravity and temperature on spaceflight experiments compared to ground based studies, and allow appropriate controls and sensitivity tests. Up to the present time, the Orbiter (Space Shuttle) has not generally provided temperature measurement instrumentation inside ambient lockers located in the Mid-deck of the Orbiter, or inside similar facilities such as Spacehab and Spacelab, but many pieces of hardware do have temperature recording capability. Most of these temperatures, however, have only been roughly measured or estimated. Such reported experimental temperatures, while accurate within a range of several degrees Celsius, are of limited utility to biological researchers. The temperature controlled lockers used in spaceflight, such as Commerical-Refrigeration Incubation Modules (C-R/IMs), severely reduce the mass and volume available for test samples and do not necessarily provide uniform thermal environments. While these test carriers avoid some of the experimental temperature variations of the ambient lockers, the number of samples which can be accommodated in these temperature controlled units is limited. In the present work, improved models of thermal prediction and control were sought. Temperatures are predicted by thermal analysis software using empirical temperatures recorded during STS-57. These temperatures are compared to data recorded throughout the mission using Ambient Temperature Recorders (ATRs) located within several payload lockers. Additional test cases are undertaken using controlled ground experiments to more precisely determine the reliability of the thermal model. The approach presented should increase the utility of various spaceflight carriers in the support of biological and material science research and ground control studies done in preparation for flight.

Birdsong, M. B.↗

NASA NTRS1993

The importance of properties in modeling

Casting and welding of superalloys, stainless steel and titanium alloys are processes which can be improved through modeling of heat flow, fluid flow, residual stress development, and microstructural evolution. These simulations require inputs of thermophysical data, some of which involves the partially or totally liquid state. In particular, these processes involve melting, flow in the liquid, and solidification. Modeling of such processes can lead to an improved understanding of defects such as shrinkage, inclusions, cracks, incomplete filling (or penetration), macrosegregation, improper grain structure, and deviations from dimensional specifications. Effective modeling can shorten process development time and improve quality. An approach to these problems is to develop efficient models; validate through correlations with thermal, distortion, and microstructural data; run parametric studies; extract knowledge based rules; and apply to adaptive closed loop control systems. With the appropriate pre- and post-processing, such analyses can be made 'user friendly'. This would include graphical user interfaces as well as realistic images and color maps. In such form, these models can be used for sensitivity analyses, which are useful in defining appropriate sensors and in the development of control strategies. Such modeling can be done at several levels, e.g., the MARO level, modeling large scale phenomena such as heat and fluid flow or material deformation; the MICRO level, modeling the development of dendrites, grains or precipitates; or at the NANO level, modeling point defects, dislocations, stacking faults, etc. There are many computational issues associated with these simulations, e.g. computational efficiency and accuracy. In addition, there are many materials issues, not the least of which is the availability of accurate high temperature thermophysical data for complex alloys. This would include latent heat of fusion, temperature dependent heat capacity and thermal conductivity (for liquid and solid), viscosity, surface tension, thermal expansion, mechanical properties, etc. Preliminary data is frequently gathered from the literature; however, this is often not available for modern alloys. If additional data are required, measurements can be used; however, these are costly, time consuming and can be erroneous due to a lack of testing standards or impure materials. Microstructural predictors can be extracted from thermal information, e.g. cooling rate and thermal gradient; the prediction of microstructure is dependent on solidus and liquidus temperature, mushy zone permeability, the solidification curve, volume changes, phase transformations, alloying effects (such as surface tension or viscosity), mold/metal reactions, metal/environment reactions, etc. Defect maps may be needed to predict the onset of shrinkage, hot cracking or 'freckling'. Constants may be needed for stress relaxation, dendrite coarsening, vaporization, etc. Visualization was used as a tool to better comprehend complex data sets associated with the analysis of directional solidification (including crystal growth) and welding. Examples include not only isotherms, but also cooling rate, growth rate and thermal gradient. The latter two are not single valued scalars, but rather time and space dependent vector fields. Efficient models were developed for both casting and welding to predict heat flow and the relationship to dendrite and grain growth. These codes include many of the non-linear effects, e.g. radiation, which dominate these processes. The home-built FDM code(s) were designed to be useful not only to the scientist, but also to the process engineer. Special output can be requested to compare directly to experimental data. Visualization procedures were developed to visualize critical results, e.g. fusion zone width at the surface opposite that where the arc is applied ('penetration'). Both elaborate and simplified distortion analyses were carried out. It is clear that extensive mechanical property data are critical in order to accurately predict residual stress patterns. A scheme is currently being developed to integrate these modeling tools into a set of control algorithms; however, the success of this approach is critically dependent on the availability of accurate high temperature thermophysical data.

Giamei, A. F.↗

NASA NTRS1991

Validation of engineering methods for predicting hypersonic vehicle controls forces and moments

This work examines the ability of the aerodynamic analysis methods contained in an industry standard conceptual design code, the Aerodynamic Preliminary Analysis System (APAS II), to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds. Predicted control forces and moments generated by various control effectors are compared with previously published wind-tunnel and flight-test data for three vehicles: the North American X-15, a hypersonic research airplane concept, and the Space Shuttle Orbiter. Qualitative summaries of the results are given for each force and moment coefficient and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage.

Maughmer, M.↗

NASA NTRS1970

Prediction of gas leakage of environmental control systems

Mathematical models of leakage configurations and various flow theories are presented with the substantive experimental test data to provide background material for future design and failure analysis. Normal-rate leakage and emergency, high-rate leakage are considered.

Jackson, J. K.↗

NASA NTRS1996

IMACCS: A Progress Report on NASA/GSFC's COTS-Based Ground Data Systems, and their Extension into New Domains

The integrated monitoring, analysis and control commercial off-the-shelf system (IMACCS) for the provision of real-time satellite command and telemetry support, orbit and attitude determination, events prediction and data trend analysis, is considered. The upgrades made to the original commercial, off-the-shelf (COTS) prototype are described. These upgrades include automation capability, and spacecraft integration and testing capability. A further extension to the prototype is the establishment of a direct radio frequency interface to a spacecraft. The systems development approach employed is described.

Scheidker, E. J.↗

NASA NTRS1943

Preliminary Correlation of the Effects of Beveled Trailing Edges on the Hinge-Moment Characteristics of Control Surfaces

A study of available data from various tests of beveled control surfaces has been made in an attempt to develop a rational method for predicting the effects of beveled trailing edges on the hinge-moment characteristics of control surfaces in both two- and three-dimensional flow. The results of the study indicated that the change in the included angle at the control-surface trailing edge formed a convenient basis on which a correlation could be made of the effects of various profile modifications on hinge-moment characteristics. It is believed that the formulas developed will allow reasonably accurate predictions of the hinge-moment characteristics of sealed beveled control surfaces if the characteristics of the original control surfaces are known. The presence of a gap at the control-surface hinge increased the effect of beveled trailing edges on the hinge-moment characteristics at small control-surface deflections but the available data were insufficient to allow as complete a correlation as was possible for sealed controls.

Purser, Paul E.↗

NASA NTRS1984

Navigation system and method

In a global positioning system (GPS), such as the NAVSTAR/GPS system, wherein the position coordinates of user terminals are obtained by processing multiple signals transmitted by a constellation of orbiting satellites, an acquisition-aiding signal generated by an earth-based control station is relayed to user terminals via a geostationary satellite to simplify user equipment. The aiding signal is FSK modulated on a reference channel slightly offset from the standard GPS channel. The aiding signal identifies satellites in view having best geometry and includes Doppler prediction data as well as GPS satellite coordinates and identification data associated with user terminals within an area being served by the control station and relay satellite. The aiding signal significantly reduces user equipment by simplifying spread spectrum signal demodulation and reducing data processing functions previously carried out at the user terminals.

Taylor, R. E.↗

NASA NTRS1992

Estimation of the end-of-life optical properties of Z-93 thermal control coating for the Space Station Freedom

This paper describes an approach to estimating the 30-year end-of-life optical properties of Z-93 white, thermal control coating on the Space Station Freedom. Estimations of the 30-year end-of-life properties of thermal control coatings are necessary for sizing the thermal radiators. The available flight and ground data and computer generated contamination models are used to predict the effects of ground handling, exposure to UV radiation, and on-orbit contamination on the optical properties of Z-93. The solar absorptance for portions of the thermal control system radiator surfaces is predicted to change from 0.15 to 0.36 after 30 years of low earth orbit environmental exposure. The infrared emittance is predicted to change from 0.92 to 0.90.

Hasegawa, Mark M.↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Acoustic Predictions of the Moog S-250 in Hover

Acoustic predictions were made on the Moog S-250 vehicle to assess one of NASA’s mid-fidelity prediction toolchains for Urban Air Mobility-type vehicles. Experimental data from a 60-foot hover test is used for comparison. The S-250 is an RPM-controlled vehicle in a quadcopter-type configuration with four sets of contra-rotating rotors and powered by eight independently controlled electric motors. The same type of rotor blade used on the S-250 was 3D laser scanned to enable the development of custom-made airfoil tables and more accurate blade loadings. Noise predictions at the first couple of blade-pass frequencies indicate a good comparison to experimental data.

Acoustic prediction↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗